發(fā)布時(shí)間：2018-03-24 01:20

  本文選題：礦井水源　切入點(diǎn)：KNN　出處：《安徽理工大學(xué)》2017年碩士論文

【摘要】：在煤礦井下,發(fā)生的水害水災(zāi)是礦井安全工作中的重點(diǎn)防治對(duì)象。突水是水災(zāi)主要的體現(xiàn),一旦發(fā)生,則會(huì)造成嚴(yán)重的人身和經(jīng)濟(jì)損失。所以,防治水害的工作是非常重要的。在水害防治工作中,對(duì)于礦井水源的識(shí)別工作也是必不可少的,對(duì)于傳統(tǒng)的識(shí)別方法,如水化學(xué)方法,其耗時(shí)長(zhǎng)、效率低等缺點(diǎn)都沒(méi)能很好地解決。針對(duì)這些情況,本文提出了利用KNN算法結(jié)合LIF技術(shù)在礦井水源識(shí)別的應(yīng)用。首先分析煤礦井下水源的由來(lái),詳細(xì)介紹其產(chǎn)生的原因與現(xiàn)階段礦井水源所處的地下層,分析對(duì)礦井安全的危害。然后對(duì)礦井水源的水樣提取做出了要求和介紹,對(duì)于礦井下水樣的提取工作,是非常困難的,而且所提取的水樣需要進(jìn)行實(shí)驗(yàn)前處理,達(dá)到實(shí)驗(yàn)所需的要求。再對(duì)實(shí)驗(yàn)所用的實(shí)驗(yàn)設(shè)備進(jìn)行了介紹,實(shí)驗(yàn)的設(shè)備是自主研制的礦用設(shè)備,目前處于實(shí)驗(yàn)室階段。利用該設(shè)備,對(duì)所采集到的礦井水源進(jìn)行光譜數(shù)據(jù)的采集,設(shè)置好設(shè)備參數(shù),保證采集過(guò)程在暗室進(jìn)行,之后將采集的光譜原始數(shù)據(jù)存儲(chǔ)在上位機(jī)中,待用。在光譜數(shù)據(jù)處理之前,需要對(duì)其進(jìn)行光譜預(yù)處理,本文采用多種預(yù)處理方式,起到對(duì)比的作用,在其中選取最佳的光譜預(yù)處理方法。本文還介紹了 KNN算法以及一些改進(jìn)的KNN算法,對(duì)于改進(jìn)的算法進(jìn)行了原理分析。并在實(shí)驗(yàn)中進(jìn)行多種改進(jìn)的KNN算法同時(shí)對(duì)光譜數(shù)據(jù)進(jìn)行處理分類,在改變K值的基礎(chǔ)上,對(duì)多種改進(jìn)KNN算法的準(zhǔn)確度進(jìn)行分析,選取最佳的KNN算法。實(shí)驗(yàn)所用到的軟件有MATLAB和SPSS,對(duì)數(shù)據(jù)處理有很大的功能,操作起來(lái)也非常簡(jiǎn)單。最后,對(duì)來(lái)自淮南某一礦區(qū)所采集的礦井水源進(jìn)行了實(shí)際的分類實(shí)驗(yàn),利用改進(jìn)的KNN算法對(duì)光譜數(shù)據(jù)進(jìn)行分類,所分類的準(zhǔn)確度非�？捎^,再次證明了 KNN算法在礦井水源識(shí)別中的應(yīng)用是非常可行的,而且具有很高的使用價(jià)值。對(duì)于KNN算法在礦井水源中的應(yīng)用,本文所提出的這種識(shí)別分類方法是第一次應(yīng)用。對(duì)于其仿真結(jié)果和實(shí)際的實(shí)驗(yàn)分析結(jié)果來(lái)說(shuō),都說(shuō)明了,KNN算法在礦井水源識(shí)別的應(yīng)用中是非常值得研究的。也充分展示了,LIF技術(shù)在此領(lǐng)域的特殊之處,能夠快速的建立模型對(duì)未知的水樣進(jìn)行識(shí)別分類。這對(duì)于今后的煤礦產(chǎn)業(yè)安全工作,起到了里程碑性的進(jìn)步。
[Abstract]:In the coal mine underground, the water disaster flood is the key prevention object in the mine safety work. Water inrush is the main embodiment of the flood. Once it occurs, it will cause serious personal and economic losses. The prevention and control of water hazards is very important. In the prevention and control of water hazards, it is also necessary for the identification of mine water sources, and for traditional identification methods, such as hydrochemical methods, it takes a long time. The shortcomings of low efficiency have not been solved well. In view of these conditions, this paper puts forward the application of KNN algorithm combined with LIF technology in mine water source identification. Firstly, the origin of underground water source in coal mine is analyzed. This paper introduces the causes of mine water source and the underground layer of mine water source at this stage, analyzes the harm to mine safety, and then makes a request and introduction to the water sample extraction of mine water source, which is very difficult to extract mine water sample. Moreover, the extracted water samples need to be treated before the experiment to meet the requirements of the experiment. Then the experimental equipment used in the experiment is introduced. The experimental equipment is a self-developed mine equipment, which is currently in the laboratory stage. To collect the spectral data of mine water source, set the parameters of the equipment to ensure that the collection process is carried out in the dark room, and then store the original spectral data in the upper computer to be used. Before the spectral data processing, In this paper, we choose the best spectral pretreatment method, and we also introduce the KNN algorithm and some improved KNN algorithm. The principle of the improved algorithm is analyzed, and several improved KNN algorithms are used to process and classify the spectral data in the experiment. On the basis of changing the K value, the accuracy of the improved KNN algorithm is analyzed. The best KNN algorithm is selected. The software used in the experiment is MATLAB and SPSS, which has great function in data processing and is very simple to operate. Finally, the actual classification experiment of mine water collected from a mining area in Huainan is carried out. Using the improved KNN algorithm to classify the spectral data, the accuracy of the classification is very considerable. It is proved that the application of the KNN algorithm in mine water source recognition is very feasible. For the application of KNN algorithm in mine water source, the method proposed in this paper is the first application. It shows that the application of KNN algorithm in mine water source identification is very worthy of study, and fully demonstrates the special features of LIF technology in this field. It can quickly establish the model to identify and classify the unknown water samples, which is a milestone progress for the future safety work of coal mine industry.
【學(xué)位授予單位】：安徽理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TD745.2

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本文編號(hào)：1656075